Learn how the National Football League (NFL) uses mathematical optimization to solve one of the hardest scheduling problems in existence.
At first glance, the NFL’s scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. Making the problem particularly hard is the necessary inclusion of thousands of constraints addressing stadium availability, travel considerations, competitive equity, and television viewership.
In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi’s mathematical optimization solver to tackle this complex scheduling problem. With mathematical optimization, NFL planners can generate and analyze more than 50,000 feasible schedules despite adding more constraints to the process every year. Now rather than spending months manually constructing one schedule, the NFL planners can focus on evaluating and comparing thousands of completed schedules to determine which should be selected as the final schedule.
View this session and learn:
- How the NFL uses mathematical optimization to solve one of the most challenging scheduling problems in existence.
- How the NFL switched from a linear to a parallel approach to optimization.